Recent research has shown that GAN-generated images are easy to detect for dedicated detection algorithms. They rely on artifacts in the Fourier representation of the generated images. As the goal for image synthesis is to produce images which are indistinguishable from real ones, this is a major drawback. We propose a novel training framework which penalizes synthesis artifacts by computing a Fourier dissimilarity between synthesized and real images. In this work, we investigate in which bandwidth in the Fourier domain the artifacts occur and show that the accuracy of StyleGAN images being detected as fake can be reduced by using an appropriate training strategy.
To reproduce our results, please follow the subsequent tutorial. The code is tested for the use on the Leonhard cluster and therefore we recommend to run the code on the Leonhard cluster as well. If you wish to run the code on another machine, please reach out to us - mschaller@ethz.ch (a few file paths will need to be adjusted).
NOTE: This readme was written to reproduce our experiments on the Leonhard cluster. The necessary code will be downloaded when the steps are followed. The code which was submitted with the report is meant for inspection purposes.
Clone the GitHub repository in the Leonhard cluster's $SCRATCH folder and update its cited submodules
cd $SCRATCH
git clone --branch submission https://github.com/hadzica/artifact_regularization_for_gans.git
cd artifact_regularization_for_gans
git submodule update --initInfo: The submodules are forked from https://github.com/PeterWang512/CNNDetection, https://github.com/genforce/genforce, and https://github.com/genforce/idinvert_pytorch, respectively.
Install conda using the following commands
cd $SCRATCH
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod +x Miniconda3-latest-Linux-x86_64.sh
./Miniconda3-latest-Linux-x86_64.shwhere you should specify the installation path when asked to and type yes when asked if the installer should intiliaze Miniconda3. Restart the shell window after conda has been successfully installed.
Create and activate the conda environment
cd $SCRATCH/artifact_regularization_for_gans
conda env create -f environment.yml
conda activate DLMake sure that you always activate DL before running scripts, files etc.
Download pre-trained weights, and pairs of FFHQ images and their corresponding optimized latent codes as described in the report.
bash scripts/download.shLoad ETH proxy:
module load eth_proxyStart training and subsequent evaluation with the fake detection network, where you should specify your <NETHZ>:
mkdir $SCRATCH/results
cd $SCRATCH/artifact_regularization_for_gans
bsub -R "rusage[mem=32768,ngpus_excl_p=1]" -R "select[gpu_mtotal0>=10240]" -W 24:00 scripts/train_eval.sh 0 1e3 cos 1e-4 10000 <NETHZ>
bsub -R "rusage[mem=32768,ngpus_excl_p=1]" -R "select[gpu_mtotal0>=10240]" -W 24:00 scripts/train_eval.sh 1 1e3 cos 1e-5 10000 <NETHZ>
bsub -R "rusage[mem=32768,ngpus_excl_p=1]" -R "select[gpu_mtotal0>=10240]" -W 24:00 scripts/train_eval.sh 1 0 cos 1e-6 10000 <NETHZ>Info: The first argument of train_eval.sh is the weight of the adversarial loss, while the second argument is the weight of the Fourier loss, for which the type of dissimilarity measure is chosen through the third argument. The fourth argument specifies the learning rate und the fifth argument denotes the number of images that are synthesized for determining the detection accuracy. We have observed very rare cases, when the three jobs interfere with each other in terms of GPU usage. If you see that one job did not produce any output files in $SCRATCH/results, then you should re-start it.
To reproduce the figures in the report based on the previous training results, please use the following commands, specifying your <NETHZ>:
cd $SCRATCH/artifact_regularization_for_gans
bsub -R "rusage[mem=32768,ngpus_excl_p=1]" -R "select[gpu_mtotal0>=10240]" -W 4:00 scripts/visualize.sh 0_1e3_cos_1e-4 17 1_1e3_cos_1e-5 10 1_0_cos_1e-6 5 <NETHZ>Info: The first, third, and fifth argument of visualize.sh correspond to the training configurations chosen previously. The second, fourth, and sixth argument are the number of training epochs for which the resulting spectra should be compared. The numerical values are chosen such that with all threee training configurations, the detection accuracy is at 70 %.
If you wish to download the created .pdf files to your local computer in order to view them, you can use the following command, specifying your <NETHZ> and the <SAVEPATH> where you would like to store the files:
scp <NETHZ>@login.leonhard.ethz.ch:/cluster/scratch/<NETHZ>/artifact_regularization_for_gans/visualization/*.pdf <SAVEPATH>Creating triplets of real images, latent codes, and fake images
As described in the report, we compute the optimized latent codes of the in-domain GAN inversion prior to the actual fine-tuning of the StyleGAN generator. The following steps can be followed to reproduce the results that have been downloaded already in the previous steps. All Installation steps should be finished beforehand.
Download FFHQ data:
cd $SCRATCH/artifact_regularization_for_gans
bash scripts/download_FFHQ.shUtilize in-domain GAN inversion to optimize latent codes for real FFHQ images and pass those through the StyleGAN generator to retrieve associated "fake" images.
bsub -R "rusage[mem=32768,ngpus_excl_p=1]" -W 120:00 < scripts/realZfake.shInfo: the results will be saved into data/reproduced